Modeling bus passenger boarding/alighting times: A stochastic approach

Abstract

This paper introduces two multilinear models that compute bus passenger boarding/alighting (BA) times at bus stops using empirical data (8341 empirical observations) from King County Metro in Seattle, Washington. The first model is a classical frequentist model, while the second is a stochastic model developed using bootstrapping together with a Cholesky decomposition. Three variables are considered in the aforementioned models to estimate passenger BA times, namely; the number of alighting passengers, the number of boarding passengers, and the number of passengers on board the bus. The models show that passenger BA times increase with an increase in all three variables (i.e. positive model coefficients). The Cholesky decomposition technique was applied to bootstrapped data to capture the model coefficient correlations with the use of only nine parameters (coefficient means, variances, and correlations) while capturing the stochasticity observed in the empirical data.

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